A safety evaluation of ruxolitinib for the treatment of polycythemia vera
Bibliographic record
Abstract
INTRODUCTION: Polycythemia Vera (PV) is a chronic myeloproliferative neoplasm hallmarked by deregulated proliferation of hematopoietic stem cells leading to prevalent expansion of red cell mass, increased rate of vascular events, splenomegaly, disease-associated symptoms, and risk of evolution to secondary myelofibrosis and blast phase. PV is pathogenetically associated with autonomously persistent activation of JAK2, which causes overproduction of blood cells and an inflammatory condition responsible for the clinical manifestations of the disease. Extensively supported by preclinical studies, targeting JAK2-dependent signaling represents a rational therapeutic approach to PV, finally leading to the approval of ruxolitinib, a JAK1/2 inhibitor. AREAS COVERED (LITERATURE RESEARCH): We analyzed reports of phase 2 and phase 3 trials with ruxolitinib in PV and relevant literature dealing with efficacy and safety aspects, including most recent real-world reports. EXPERT OPINION: Ruxolitinib is the only JAK2 inhibitor approved for the treatment of PV with well-known efficacy for splenomegaly, symptoms, and potentially reduction of vascular events. The treatment regimen is notably manageable and safe, with the most prevalent side effects primarily encompassing myelosuppression, hyperlipidemia, non-melanoma skin cancer and infections, mainly reactivation of Herpes Zoster. These effects necessitate ongoing surveillance and proactive preventive measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".